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<div class="header">
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<a href="#func-members">Functions</a>  </div>
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<div class="title">Concatenation Functions<div class="ingroups"><a class="el" href="group__groupNN.html">Neural Network Functions</a></div></div>  </div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:gaf2ec7d439726caa96e0b3dc989b34d64"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Concatenation.html#gaf2ec7d439726caa96e0b3dc989b34d64">arm_concatenation_s8_w</a> (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint32_t offset_w)</td></tr>
<tr class="memdesc:gaf2ec7d439726caa96e0b3dc989b34d64"><td class="mdescLeft">&#160;</td><td class="mdescRight">int8/uint8 concatenation function to be used for concatenating N-tensors along the W axis (Batch size) This function should be called for each input tensor to concatenate. The argument offset_w will be used to store the input tensor in the correct position in the output tensor  <a href="#gaf2ec7d439726caa96e0b3dc989b34d64">More...</a><br/></td></tr>
<tr class="separator:gaf2ec7d439726caa96e0b3dc989b34d64"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gac06ac3c87cad1cfb14aa24b19124fcfd"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Concatenation.html#gac06ac3c87cad1cfb14aa24b19124fcfd">arm_concatenation_s8_x</a> (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_x, const uint32_t offset_x)</td></tr>
<tr class="memdesc:gac06ac3c87cad1cfb14aa24b19124fcfd"><td class="mdescLeft">&#160;</td><td class="mdescRight">int8/uint8 concatenation function to be used for concatenating N-tensors along the X axis This function should be called for each input tensor to concatenate. The argument offset_x will be used to store the input tensor in the correct position in the output tensor  <a href="#gac06ac3c87cad1cfb14aa24b19124fcfd">More...</a><br/></td></tr>
<tr class="separator:gac06ac3c87cad1cfb14aa24b19124fcfd"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:gaf0b76b039f66f34ec99503193a015ff6"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Concatenation.html#gaf0b76b039f66f34ec99503193a015ff6">arm_concatenation_s8_y</a> (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_y, const uint32_t offset_y)</td></tr>
<tr class="memdesc:gaf0b76b039f66f34ec99503193a015ff6"><td class="mdescLeft">&#160;</td><td class="mdescRight">int8/uint8 concatenation function to be used for concatenating N-tensors along the Y axis This function should be called for each input tensor to concatenate. The argument offset_y will be used to store the input tensor in the correct position in the output tensor  <a href="#gaf0b76b039f66f34ec99503193a015ff6">More...</a><br/></td></tr>
<tr class="separator:gaf0b76b039f66f34ec99503193a015ff6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ga9ae180a44e18ee58936dba1e0564560b"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__Concatenation.html#ga9ae180a44e18ee58936dba1e0564560b">arm_concatenation_s8_z</a> (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_z, const uint32_t offset_z)</td></tr>
<tr class="memdesc:ga9ae180a44e18ee58936dba1e0564560b"><td class="mdescLeft">&#160;</td><td class="mdescRight">int8/uint8 concatenation function to be used for concatenating N-tensors along the Z axis This function should be called for each input tensor to concatenate. The argument offset_z will be used to store the input tensor in the correct position in the output tensor  <a href="#ga9ae180a44e18ee58936dba1e0564560b">More...</a><br/></td></tr>
<tr class="separator:ga9ae180a44e18ee58936dba1e0564560b"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Description</h2>
<h2 class="groupheader">Function Documentation</h2>
<a class="anchor" id="gaf2ec7d439726caa96e0b3dc989b34d64"></a>
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          <td class="memname">void arm_concatenation_s8_w </td>
          <td>(</td>
          <td class="paramtype">const int8_t *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_x</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_y</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_z</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_w</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int8_t *&#160;</td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint32_t&#160;</td>
          <td class="paramname"><em>offset_w</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>i.e. offset_w = 0 for(i = 0 i &lt; num_input_tensors; ++i) { arm_concatenation_s8_w(&amp;input[i], ..., &amp;output, ..., ..., offset_w) offset_w += input_w[i] }</p>
<p>This function assumes that the output tensor has:</p>
<ol type="1">
<li>The same width of the input tensor</li>
<li>The same height of the input tensor</li>
<li>The same number o channels of the input tensor</li>
</ol>
<p>Unless specified otherwise, arguments are mandatory.</p>
<dl class="section note"><dt>Note</dt><dd>This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it does not involve any arithmetic operation</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Pointer to input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_x</td><td>Width of input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_y</td><td>Height of input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_z</td><td>Channels in input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_w</td><td>Batch size in input tensor </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Pointer to output tensor. Expected to be at least input_x * input_y * input_z * input_w bytes. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">offset_w</td><td>The offset on the W axis to start concatenating the input tensor It is user responsibility to provide the correct value </td></tr>
  </table>
  </dd>
</dl>

<p>References <a class="el" href="arm__nnsupportfunctions_8h.html#a085459f6280c8f586ddd64998dece532">arm_memcpy_q7()</a>.</p>

</div>
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<a class="anchor" id="gac06ac3c87cad1cfb14aa24b19124fcfd"></a>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void arm_concatenation_s8_x </td>
          <td>(</td>
          <td class="paramtype">const int8_t *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_x</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_y</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_z</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_w</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int8_t *&#160;</td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>output_x</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint32_t&#160;</td>
          <td class="paramname"><em>offset_x</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>i.e. offset_x = 0 for(i = 0 i &lt; num_input_tensors; ++i) { arm_concatenation_s8_x(&amp;input[i], ..., &amp;output, ..., ..., offset_x) offset_x += input_x[i] }</p>
<p>This function assumes that the output tensor has:</p>
<ol type="1">
<li>The same height of the input tensor</li>
<li>The same number of channels of the input tensor</li>
<li>The same batch size of the input tensor</li>
</ol>
<p>Unless specified otherwise, arguments are mandatory.</p>
<dl class="section note"><dt>Note</dt><dd>This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it does not involve any arithmetic operation</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Pointer to input tensor. Input tensor must not overlap with the output tensor. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_x</td><td>Width of input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_y</td><td>Height of input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_z</td><td>Channels in input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_w</td><td>Batch size in input tensor </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Pointer to output tensor. Expected to be at least (input_x * input_y * input_z * input_w) + offset_x bytes. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">output_x</td><td>Width of output tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">offset_x</td><td>The offset (in number of elements) on the X axis to start concatenating the input tensor It is user responsibility to provide the correct value</td></tr>
  </table>
  </dd>
</dl>
<p><b> Input constraints</b> offset_x is less than output_x </p>

<p>References <a class="el" href="arm__nnsupportfunctions_8h.html#a085459f6280c8f586ddd64998dece532">arm_memcpy_q7()</a>.</p>

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          <td class="memname">void arm_concatenation_s8_y </td>
          <td>(</td>
          <td class="paramtype">const int8_t *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_x</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_y</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_z</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_w</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int8_t *&#160;</td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>output_y</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint32_t&#160;</td>
          <td class="paramname"><em>offset_y</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>i.e. offset_y = 0 for(i = 0 i &lt; num_input_tensors; ++i) { arm_concatenation_s8_y(&amp;input[i], ..., &amp;output, ..., ..., offset_y) offset_y += input_y[i] }</p>
<p>This function assumes that the output tensor has:</p>
<ol type="1">
<li>The same width of the input tensor</li>
<li>The same number of channels of the input tensor</li>
<li>The same batch size of the input tensor</li>
</ol>
<p>Unless specified otherwise, arguments are mandatory.</p>
<dl class="section note"><dt>Note</dt><dd>This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it does not involve any arithmetic operation</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Pointer to input tensor. Input tensor must not overlap with the output tensor. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_x</td><td>Width of input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_y</td><td>Height of input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_z</td><td>Channels in input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_w</td><td>Batch size in input tensor </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Pointer to output tensor. Expected to be at least (input_z * input_w * input_x * input_y) + offset_y bytes. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">output_y</td><td>Height of output tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">offset_y</td><td>The offset on the Y axis to start concatenating the input tensor It is user responsibility to provide the correct value</td></tr>
  </table>
  </dd>
</dl>
<p><b> Input constraints</b> offset_y is less than output_y </p>

<p>References <a class="el" href="arm__nnsupportfunctions_8h.html#a085459f6280c8f586ddd64998dece532">arm_memcpy_q7()</a>.</p>

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          <td class="memname">void arm_concatenation_s8_z </td>
          <td>(</td>
          <td class="paramtype">const int8_t *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_x</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_y</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_z</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>input_w</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int8_t *&#160;</td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint16_t&#160;</td>
          <td class="paramname"><em>output_z</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const uint32_t&#160;</td>
          <td class="paramname"><em>offset_z</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">
<p>i.e. offset_z = 0 for(i = 0 i &lt; num_input_tensors; ++i) { arm_concatenation_s8_z(&amp;input[i], ..., &amp;output, ..., ..., offset_z) offset_z += input_z[i] }</p>
<p>This function assumes that the output tensor has:</p>
<ol type="1">
<li>The same width of the input tensor</li>
<li>The same height of the input tensor</li>
<li>The same batch size of the input tensor</li>
</ol>
<p>Unless specified otherwise, arguments are mandatory.</p>
<dl class="section note"><dt>Note</dt><dd>This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it does not involve any arithmetic operation</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>Pointer to input tensor. Input tensor must not overlap with output tensor. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_x</td><td>Width of input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_y</td><td>Height of input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_z</td><td>Channels in input tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">input_w</td><td>Batch size in input tensor </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">output</td><td>Pointer to output tensor. Expected to be at least (input_x * input_y * input_z * input_w) + offset_z bytes. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">output_z</td><td>Channels in output tensor </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">offset_z</td><td>The offset on the Z axis to start concatenating the input tensor It is user responsibility to provide the correct value</td></tr>
  </table>
  </dd>
</dl>
<p><b> Input constraints</b> offset_z is less than output_z </p>

<p>References <a class="el" href="arm__nnsupportfunctions_8h.html#a085459f6280c8f586ddd64998dece532">arm_memcpy_q7()</a>.</p>

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